| Téléchargement | - Voir la version finale : Human or neural translation? (PDF, 389 Kio)
- Voir la version de l’auteur : Human or neural translation? (PDF, 354 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.18653/v1/2020.coling-main.576 |
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| Auteur | Rechercher : Bhardwaj, Shivendra; Rechercher : Alfonso-Hermelo, David; Rechercher : Langlais, Phillippe; Rechercher : Bernier-Colborne, Gabriel1; Rechercher : Goutte, Cyril1; Rechercher : Simard, Michel1 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | COLING2020: 28th International Conference on Computational Linguistics, Dec. 8-13, 2020, Barcelona, Spain (held online) |
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| Résumé | Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts. We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85% in-domain and 75% out-of-domain. |
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| Date de publication | 2020-12-13 |
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| Date de création | 2020-12-13 |
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| Maison d’édition | International Committee on Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3299ef25-9711-47c2-9b87-19b38b4323e5 |
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| Enregistrement créé | 2021-01-13 |
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| Enregistrement modifié | 2021-01-15 |
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